from sklearn.preprocessing import StandardScaler
X=StandardScaler().fit(X).transform(X)
from sklearn.decomposition import PCA
pca = PCA(n_components=2)
X_p =pca.fit(X).transform(X)
ax = plt.figure()
for c, i, target_name in zip("rgb", [0, 1, 2], data.target_names):
    plt.scatter(X_p[y == i, 0], X_p[y == i, 1], c=c, label=target_name)
plt.xlabel('Dimension1')
plt.ylabel('Dimension2')
plt.title("wine-standard-PCA")
plt.legend()
plt.show()